{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc6be60e",
   "metadata": {},
   "source": [
    "# 新的实验\n",
    "\n",
    "我后来想了想，我们原来那个实验抗过拟合效果不好，实际上并不是抗过拟合效果不好。  \n",
    "而是本来就没有什么过拟合，没有“抗”的必要。  \n",
    "所以我干了一个新的实验，把数据集缩小了，人为降低了信噪比，给线性模型添了点麻烦，然后再进行实验。  \n",
    "这个结果很好的表现了 Lasso 和 Ridge 回归的抗过拟合效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5222014f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso, RidgeCV, LassoCV, LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6285d6bd",
   "metadata": {},
   "source": [
    "## 导入数据\n",
    "\n",
    "同时将数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f1afc64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   longitude         100 non-null    float64\n",
      " 1   latitude          100 non-null    float64\n",
      " 2   housingMedianAge  100 non-null    float64\n",
      " 3   totalRooms        100 non-null    float64\n",
      " 4   totalBedrooms     100 non-null    float64\n",
      " 5   population        100 non-null    float64\n",
      " 6   households        100 non-null    float64\n",
      " 7   medianIncome      100 non-null    float64\n",
      " 8   medianHouseValue  100 non-null    float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 7.2 KB\n"
     ]
    }
   ],
   "source": [
    "feature_names = [\n",
    "    'longitude',\n",
    "    'latitude',\n",
    "    'housingMedianAge',\n",
    "    'totalRooms',\n",
    "    'totalBedrooms',\n",
    "    'population',\n",
    "    'households',\n",
    "    'medianIncome',\n",
    "    'medianHouseValue'\n",
    "]\n",
    "df = pd.read_csv('cal_housing.data', header=None, names=feature_names)\n",
    "df = df.iloc[0:100]\n",
    "X = df.values[:, 0:8]\n",
    "y = df.values[:, 8]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "full_scaler = StandardScaler()\n",
    "X_full = full_scaler.fit_transform(X)\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d11bf84",
   "metadata": {},
   "source": [
    "## 计算 Coef_Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3b0b1a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.034e+11, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.023e+11, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+11, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.000e+11, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.871e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.732e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.582e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.422e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.252e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.069e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.876e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.670e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.453e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.224e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.983e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.731e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.468e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.195e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.912e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.620e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.321e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.015e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.705e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.391e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.076e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.761e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.448e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.139e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.835e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.539e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.252e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.975e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.711e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.459e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.998e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.790e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.596e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.418e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+10, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.719e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.506e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.420e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.450e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.590e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.831e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.162e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.577e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.066e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.622e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.237e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.905e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.618e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.373e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.162e+09, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.827e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.296e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.994e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.889e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.953e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.162e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.494e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.931e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.457e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.724e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.208e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+08, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.468e+07, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/home/goldfish/miniconda3/envs/MCM/lib/python3.13/site-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.094e+07, tolerance: 6.294e+07\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "alphas_lasso = np.logspace(-2, 6, 200)\n",
    "alphas_ridge = np.logspace(-2, 6, 200)\n",
    "\n",
    "lasso = Lasso()\n",
    "coef_path_lasso = []\n",
    "for alpha in alphas_lasso:\n",
    "    lasso.set_params(alpha=alpha)\n",
    "    lasso.fit(X_full, y)\n",
    "    coef_path_lasso.append(lasso.coef_)\n",
    "coef_path_lasso = np.array(coef_path_lasso)\n",
    "\n",
    "ridge = Ridge()\n",
    "coef_path_ridge = []\n",
    "for alpha in alphas_ridge:\n",
    "    ridge.set_params(alpha=alpha)\n",
    "    ridge.fit(X_full, y)\n",
    "    coef_path_ridge.append(ridge.coef_)\n",
    "coef_path_ridge = np.array(coef_path_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d98fd4b",
   "metadata": {},
   "source": [
    "上面这段是说目标没有收敛，估计是数据集太小了，对有些 $lambda$ 不友好，但看下面的图，似乎并没有影响什么  \n",
    "如果大家有什么发现，可以提出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa88af09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 55910.14815428, -14362.1150276 ,   1501.3777283 , ...,\n",
       "         16679.08375262,  71768.82577354,  24313.79523759],\n",
       "       [ 55910.14841735, -14362.10450948,   1501.37199756, ...,\n",
       "         16679.05425251,  71768.56604387,  24313.7909597 ],\n",
       "       [ 55910.14870594, -14362.09297125,   1501.36571101, ...,\n",
       "         16679.02189131,  71768.28112411,  24313.78626692],\n",
       "       ...,\n",
       "       [     0.        ,      0.        ,      0.        , ...,\n",
       "             0.        ,      0.        ,      0.        ],\n",
       "       [     0.        ,      0.        ,      0.        , ...,\n",
       "             0.        ,      0.        ,      0.        ],\n",
       "       [     0.        ,      0.        ,      0.        , ...,\n",
       "             0.        ,      0.        ,      0.        ]],\n",
       "      shape=(200, 8))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_path_lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d23b0a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(alphas_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a757cc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_df = pd.DataFrame(coef_path_lasso, alphas_lasso, columns=feature_names[0:8])\n",
    "ridge_df = pd.DataFrame(coef_path_ridge, alphas_ridge, columns=feature_names[0:8])\n",
    "lasso_df.to_excel('lasso_coef_path.xlsx')\n",
    "ridge_df.to_excel('ridge_coef_path.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860f33dc",
   "metadata": {},
   "source": [
    "## 画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c307aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alphas_lasso, coef_path_lasso)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('Coefficient Value')\n",
    "plt.title('Lasso Coefficient Path')\n",
    "plt.legend(feature_names, ncol=1, loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d02786c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(alphas_ridge, coef_path_ridge)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Alpha')\n",
    "plt.ylabel('Coefficient Value')\n",
    "plt.title('Ridge Coefficient Path')\n",
    "plt.legend(feature_names, ncol=1, loc=(1.1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ecfd0e",
   "metadata": {},
   "source": [
    "这里就不多做分析了，直接看结果吧。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1df8423c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.iloc[:, 0:8].corr()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.imshow(correlation_matrix, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "\n",
    "plt.xticks(np.arange(8), labels=feature_names[0:8], rotation=45, rotation_mode='anchor', ha='right')\n",
    "plt.yticks(np.arange(8), labels=feature_names[0:8])\n",
    "plt.title('Correlation coefficient heat map')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c28bf9ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housingMedianAge</th>\n",
       "      <th>totalRooms</th>\n",
       "      <th>totalBedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>medianIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.585545</td>\n",
       "      <td>0.031701</td>\n",
       "      <td>0.466779</td>\n",
       "      <td>0.350136</td>\n",
       "      <td>0.337001</td>\n",
       "      <td>0.379758</td>\n",
       "      <td>0.596947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0.585545</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.355035</td>\n",
       "      <td>0.303702</td>\n",
       "      <td>0.054079</td>\n",
       "      <td>0.110434</td>\n",
       "      <td>0.089871</td>\n",
       "      <td>0.554305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housingMedianAge</th>\n",
       "      <td>0.031701</td>\n",
       "      <td>0.355035</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.033254</td>\n",
       "      <td>-0.135024</td>\n",
       "      <td>-0.062477</td>\n",
       "      <td>-0.139631</td>\n",
       "      <td>0.080604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalRooms</th>\n",
       "      <td>0.466779</td>\n",
       "      <td>0.303702</td>\n",
       "      <td>-0.033254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.869301</td>\n",
       "      <td>0.920196</td>\n",
       "      <td>0.879570</td>\n",
       "      <td>0.314235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalBedrooms</th>\n",
       "      <td>0.350136</td>\n",
       "      <td>0.054079</td>\n",
       "      <td>-0.135024</td>\n",
       "      <td>0.869301</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.944963</td>\n",
       "      <td>0.995319</td>\n",
       "      <td>0.082465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.337001</td>\n",
       "      <td>0.110434</td>\n",
       "      <td>-0.062477</td>\n",
       "      <td>0.920196</td>\n",
       "      <td>0.944963</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.944997</td>\n",
       "      <td>0.092586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.379758</td>\n",
       "      <td>0.089871</td>\n",
       "      <td>-0.139631</td>\n",
       "      <td>0.879570</td>\n",
       "      <td>0.995319</td>\n",
       "      <td>0.944997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medianIncome</th>\n",
       "      <td>0.596947</td>\n",
       "      <td>0.554305</td>\n",
       "      <td>0.080604</td>\n",
       "      <td>0.314235</td>\n",
       "      <td>0.082465</td>\n",
       "      <td>0.092586</td>\n",
       "      <td>0.110095</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  longitude  latitude  housingMedianAge  totalRooms  \\\n",
       "longitude          1.000000  0.585545          0.031701    0.466779   \n",
       "latitude           0.585545  1.000000          0.355035    0.303702   \n",
       "housingMedianAge   0.031701  0.355035          1.000000   -0.033254   \n",
       "totalRooms         0.466779  0.303702         -0.033254    1.000000   \n",
       "totalBedrooms      0.350136  0.054079         -0.135024    0.869301   \n",
       "population         0.337001  0.110434         -0.062477    0.920196   \n",
       "households         0.379758  0.089871         -0.139631    0.879570   \n",
       "medianIncome       0.596947  0.554305          0.080604    0.314235   \n",
       "\n",
       "                  totalBedrooms  population  households  medianIncome  \n",
       "longitude              0.350136    0.337001    0.379758      0.596947  \n",
       "latitude               0.054079    0.110434    0.089871      0.554305  \n",
       "housingMedianAge      -0.135024   -0.062477   -0.139631      0.080604  \n",
       "totalRooms             0.869301    0.920196    0.879570      0.314235  \n",
       "totalBedrooms          1.000000    0.944963    0.995319      0.082465  \n",
       "population             0.944963    1.000000    0.944997      0.092586  \n",
       "households             0.995319    0.944997    1.000000      0.110095  \n",
       "medianIncome           0.082465    0.092586    0.110095      1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix.to_excel(\"Correlation coefficient heat map.xlsx\")\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab60017",
   "metadata": {},
   "source": [
    "## 导出最优模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3c0014a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cv 会自动选择最优参数\n",
    "lasso_cv = LassoCV(alphas=alphas_lasso, cv=5)\n",
    "lasso_cv.fit(X_train, y_train)\n",
    "best_alpha_lasso = lasso_cv.alpha_\n",
    "\n",
    "ridge_cv = RidgeCV(alphas=alphas_ridge, cv=5)\n",
    "ridge_cv.fit(X_train, y_train)\n",
    "best_alpha_ridge = ridge_cv.alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367dbb29",
   "metadata": {},
   "source": [
    "自动选择最优参数时，是用的 K折交叉验证 或 留一法，也就是说其中会自动将整个数据集划分训练集和测试集。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "427f0d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housingMedianAge</th>\n",
       "      <th>totalRooms</th>\n",
       "      <th>totalBedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>medianIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear</th>\n",
       "      <td>55667.055925</td>\n",
       "      <td>-22401.333569</td>\n",
       "      <td>4074.919655</td>\n",
       "      <td>1184.994411</td>\n",
       "      <td>-134270.976675</td>\n",
       "      <td>15970.396190</td>\n",
       "      <td>109866.899544</td>\n",
       "      <td>16512.544827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>45463.748544</td>\n",
       "      <td>-4620.484642</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-2244.824171</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>13026.671310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>45452.162925</td>\n",
       "      <td>-10226.562276</td>\n",
       "      <td>776.315936</td>\n",
       "      <td>3390.157222</td>\n",
       "      <td>-8875.007940</td>\n",
       "      <td>2590.535757</td>\n",
       "      <td>-1594.021520</td>\n",
       "      <td>16915.162163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           longitude      latitude  housingMedianAge   totalRooms  \\\n",
       "Linear  55667.055925 -22401.333569       4074.919655  1184.994411   \n",
       "Lasso   45463.748544  -4620.484642          0.000000    -0.000000   \n",
       "Ridge   45452.162925 -10226.562276        776.315936  3390.157222   \n",
       "\n",
       "        totalBedrooms    population     households  medianIncome  \n",
       "Linear -134270.976675  15970.396190  109866.899544  16512.544827  \n",
       "Lasso    -2244.824171     -0.000000      -0.000000  13026.671310  \n",
       "Ridge    -8875.007940   2590.535757   -1594.021520  16915.162163  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha=best_alpha_lasso)\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "ridge = Ridge(alpha=best_alpha_ridge)\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "linear = LinearRegression()\n",
    "linear.fit(X_train, y_train)\n",
    "\n",
    "pd.DataFrame([linear.coef_.reshape([8]), lasso.coef_.reshape([8]), ridge.coef_.reshape([8])],\n",
    "             ['Linear', 'Lasso', 'Ridge'], columns=feature_names[0:8])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183b8d2e",
   "metadata": {},
   "source": [
    "## 评估数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e62d294",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lasso_train = lasso_cv.predict(X_train)\n",
    "y_pred_lasso_test = lasso_cv.predict(X_test)\n",
    "mse_lasso_train = mean_squared_error(y_train, y_pred_lasso_train)\n",
    "mse_lasso_test = mean_squared_error(y_test, y_pred_lasso_test)\n",
    "r2_lasso_train = r2_score(y_train, y_pred_lasso_train)\n",
    "r2_lasso_test = r2_score(y_test, y_pred_lasso_test)\n",
    "\n",
    "y_pred_ridge_train = ridge_cv.predict(X_train)\n",
    "y_pred_ridge_test = ridge_cv.predict(X_test)\n",
    "mse_ridge_train = mean_squared_error(y_train, y_pred_ridge_train)\n",
    "mse_ridge_test = mean_squared_error(y_test, y_pred_ridge_test)\n",
    "r2_ridge_train = r2_score(y_train, y_pred_ridge_train)\n",
    "r2_ridge_test = r2_score(y_test, y_pred_ridge_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d4298b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_linear_train = linear.predict(X_train)\n",
    "y_pred_linear_test = linear.predict(X_test)\n",
    "mse_linear_train = mean_squared_error(y_train, y_pred_linear_train)\n",
    "mse_linear_test = mean_squared_error(y_test, y_pred_linear_test)\n",
    "r2_linear_train = r2_score(y_train, y_pred_linear_train)\n",
    "r2_linear_test = r2_score(y_test, y_pred_linear_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "358fcae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>linear</th>\n",
       "      <th>Lasso</th>\n",
       "      <th>Ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alpha</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.872039e+03</td>\n",
       "      <td>9.437878e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train MSE</td>\n",
       "      <td>2.373182e+09</td>\n",
       "      <td>2.637399e+09</td>\n",
       "      <td>2.555681e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test MSE</td>\n",
       "      <td>2.635914e+09</td>\n",
       "      <td>2.369980e+09</td>\n",
       "      <td>2.405203e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train R2</td>\n",
       "      <td>5.810293e-01</td>\n",
       "      <td>5.343835e-01</td>\n",
       "      <td>5.488102e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test R2</td>\n",
       "      <td>6.881902e-01</td>\n",
       "      <td>7.196483e-01</td>\n",
       "      <td>7.154817e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Tag        linear         Lasso         Ridge\n",
       "0      Alpha  0.000000e+00  3.872039e+03  9.437878e+00\n",
       "1  train MSE  2.373182e+09  2.637399e+09  2.555681e+09\n",
       "2   test MSE  2.635914e+09  2.369980e+09  2.405203e+09\n",
       "3   train R2  5.810293e-01  5.343835e-01  5.488102e-01\n",
       "4    test R2  6.881902e-01  7.196483e-01  7.154817e-01"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Tag', 'linear', 'Lasso', 'Ridge']\n",
    "output = [\n",
    "    ['Alpha', 0, best_alpha_lasso, best_alpha_ridge],\n",
    "    ['train MSE', mse_linear_train, mse_lasso_train, mse_ridge_train],\n",
    "    ['test MSE', mse_linear_test, mse_lasso_test, mse_ridge_test],\n",
    "    ['train R2', r2_linear_train, r2_lasso_train, r2_ridge_train],\n",
    "    ['test R2', r2_linear_test, r2_lasso_test, r2_ridge_test]\n",
    "]\n",
    "pd.DataFrame(output, columns=names)"
   ]
  }
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